Due to the integration of renewable energy sources such as wind turbines, significant technical challenge exists for the energy management in the future power distribution systems and/or microgrids. The efficiency and reliability of the energy management may be jeopardized by the randomness of the power production from renewable energy sources. In order to address this challenge and to harness renewable power, community energy storage (CES) systems with dispatchable capacities can be installed to buffer the intermittent supply from renewable energy sources In Part I, we focus on the stochastic model of CES system with wind power generation. The power generation of each wind turbine is modeled using a Markov modulated rate process (MMRP), while the CES system is modeled as a queuing system. Based on a diffusion approximation of the queue length, a closed-form representation of the cumulative distribution function (CDF) of the SoC of the CES system can be derived. In Part II, we focus on the optimal energy management of the CES systems in a microgrid. During the normal operation of the microgrid, the dispatchable outputs of the CES systems are controlled to minimize the overall operation cost of the microgrid. When a fault occurs in the main grid, the microgrid operates in an islanded mode, and energy stored in the CES systems can be utilized to supply the loads in the microgrid for reliability improvement. To control the amount of energy stored in the CES systems, two kinds of SoC thresholds are introduced, which correspond to hard reservation and soft reservation of energy. Accordingly, the stochastic model of the CES system developed in Part I is extended to embed the impact of the two kinds of thresholds. To take account of the potential bias in the forecast of wind power generation, the energy management problem is solved based on a general robust optimization technique. The performance of the stochastic model and optimization technique is evaluated based on the IEEE 123 bus test feeder as well as the wind power generation data of Changling Wind Farm.

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